On comparing statistical and set-based methods in sensor data fusion

The theoretical and practical considerations of two common sensor data fusion methodologies (set based and statistically based parameter estimation) are compared. Their convergence behavior for a variety of simulated problems is examined. Robot localization systems implemented using both methods are described, and their performance is compared. It is concluded that set-based methods have performance that sometimes exceeds that of statistical methods, although this result is highly problem-dependent. These problem dependencies are characterized